The use of data analysis tools has increased dramatically as society becomes dependent on digital information storage. In e-commerce and other Internet and non-Internet applications, databases are generated and maintained that have astronomically large amounts of information. Such information is typically analyzed, or “mined,” to learn additional information regarding customers, users, products, etc. This information allows businesses and other users to better implement their products and/or ideas.
Electronic commerce is a staple for most conceivable types of businesses. People have come to expect that their favorite stores not only have brick and mortar business locations, but that they can also be accessed “online,” typically via the Internet's World Wide Web. The Web allows customers to view graphical representations of a business' store and products. Ease of use from the home and convenient purchasing methods, typically lead to increased sales. Buyers enjoy the freedom of being able to comparison shop without spending time and money to drive from store to store.
Online commerce continues to be improved to bring a more enjoyable buying experience to online buyers. Often, websites require a “log in” and/or utilize a “cookie” to track which buyer is looking at their website. With this information, a business can track purchase parameters such as type, size, quantity, and purchasing frequency. This is valuable information because it allows a company to forecast future sales and to determine what goods are of the most interest to online buyers. Sometimes, however, people tend to exhibit distinct individualism in their tastes, whether for purchases or other activities. For example, a company who sells paper online might assume that their buyers are utilizing it for craft projects. Since the company also sells crayons, they may include an advertisement for crayons next to their paper advertisement on their website. In actuality, however, some customers may be purchasing the paper for business office use, and the crayon advertisement may even turn some customers away due to the fact that the company seems to not understand their individual needs correctly. Had the company, instead, offered staples and/or paper clips along with the paper at the appropriate customer, they might have seen increased sales for all of their products as those buyers might perceive their store as a “one-stop shop” for all of their business office supply needs.
Prior to the advent of online selling, a salesperson would typically approach a customer and ask them a series of questions to better understand their likes and dislikes along with their prior purchasing habits. Through this interaction, the salesperson is able to determine suggestions for products this particular customer might like. This same type of “associative selling” is also just as important to online merchants. However, there is no salesperson to “size up” a customer online and determine their needs and wants. Instead, programs are utilized to determine suggestions for online buyers when they visit a business' website. For example, consider an online buyer who previously bought a dog bowl and a dog bone. Probabilities can be determined that show that it is likely that this person owns a dog. The person might, therefore, be interested in dog related items such as dog collars, leashes, and brushes. Since these items are brought to the attention of the buyer, if it matches their needs, they are more likely to purchase those items than, for instance, an advertisement for catnip or a bird feeder.
Although associative type selling is extremely advantageous, it is also generally very difficult to actually determine associations for distinct individuals. This is generally due to complex computing requirements, difficulty in accessing and retrieving the necessary information, and/or long computational calculation times. If a method is inaccurate, it can possibly drive customers away, causing losses in sales. Just like with a good salesperson, correctly associated products can lead to increased sales, while, like a bad salesperson, incorrectly associated products may cause a decrease in sales. Therefore, it is important to have an accurate means to associate various products/items for diverse individuals. This includes those with esoteric tastes who visit a website only once in a great while, along with those who have more traditional tastes and buy frequently from the same website.
Techniques that facilitate in determining preferences of a user are also known as “collaborative filtering.” A collaborative filtering system can produce recommendations by determining similarities between a user's preferences. The value of this type of information increases daily as society moves towards an electronic oriented environment. Preferences can be utilized in any number of ways such as by computers, televisions, satellite radios, and other devices that lend themselves to the potential of having interactivity with a user.